Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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xrpl-agent-wallet
by XRPLFUse this skill whenever an agent needs to load, sign, or submit a transaction to the XRP Ledger (XRPL) on a user's behalf. This skill owns the full wallet lifecycle - first-time wallet generation (writing the seed safely to .env, never to chat), key loading, the signing ceremony, human confirmation, and reliable submission. It does NOT construct transactions; a separate XRPL transactions skill (or the developer) provides the transaction object. Trigger on signing phrases: wallet.sign, submitAndWait, submit, xrpl.Wallet, a seed/secret being loaded, a tx_blob being produced, "send XRP", "sign this transaction", "submit to the ledger", "have the agent pay", "let the agent transact". Also trigger on onboarding phrases: "create a wallet", "generate a wallet", "I need a wallet", "set up a wallet", "get started with XRPL", "new account", "testnet wallet", or any request to produce an XRPL address for the first time. If an XRPL transaction is going to be signed, or if the user needs a wallet to begin, this skill app
xrpl-payments
by XRPLFXRPL payments playbook for AI agent developers. Covers the full developer journey: wallet setup, XRP payments, RLUSD and IOU token payments, cross-currency payments, escrow, agentic best practices (SourceTag, Memos, audit trail), and testnet-to-mainnet migration. Use this skill whenever a user asks about sending XRP or RLUSD, trust lines, xrpl-py, xrpl.js, agentic transactions, SourceTag, the XRPL AI Starter Kit, X402 payments on XRPL, or building any payment workflow on the XRP Ledger. When in doubt, load this skill — general training data for XRPL is often outdated or imprecise. This skill constructs transactions. The XRPL Agent Wallet skill signs and submits them. For wallet creation, key loading, or anything involving a seed or private key, defer to the XRPL Agent Wallet skill.
generate-release-notes
by XRPLFGenerate and sort rippled release notes from GitHub commit history
batch-deps-upgrade
by XRPLFBatch all open Dependabot dependency upgrade PRs into a single PR
batch-deps-upgrade
by XRPLFBatch all open Dependabot dependency upgrade PRs into a single PR
batch-deps-upgrade
by XRPLFBatch all open Dependabot dependency upgrade PRs into a single PR
xrpl-standards
by XRPLFReference for any XRPL Standard (XLS-N) when implementing or reviewing XRPL protocol features. Trigger on: XLS number (XLS-30, XLS-70), amendment name (AMM, Credentials, MPT, DID, NFToken, Batch, Escrow, Clawback, Firewall, Permissioned DEX), transaction type (AMMCreate, CredentialCreate, NFTokenMint, DelegateSet, PermissionedDomainSet, BatchSubmit), or ledger object name (Credential, AMM, MPToken, Delegate, Oracle, Bridge).
code-review
by XRPLFReview the current branch's diff against go-* anti-pattern rules and xrpl-standards specs. Spawns focused subagents per changed package, plus a single XRPL-domain reviewer when protocol files are touched, and synthesizes findings cross-cutting. Use when the user types /code-review, finishes a task and wants a self-review, or fetches a teammate's branch and wants to audit it before merging.
go-performance
by XRPLFUse this skill when optimizing Go performance, writing benchmarks, using the standard library (HTTP, JSON, SQL, time), or ensuring production-quality testing.
go-errors
by XRPLFUse this skill when writing, reviewing, or debugging Go error handling — wrapping errors, sentinel errors, error type checking, panicking, or handling errors in defer.
go-design
by XRPLFUse this skill when designing Go APIs, organizing packages, writing functions or methods, choosing receiver types, or structuring a Go project.
go-data
by XRPLFUse this skill when working with Go slices, maps, integers, strings, range loops, or defer in loops to avoid common data type and control flow pitfalls.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.